Feature learning is one of the sustained and prominent topics in the machine learning group. Feature representations are most required to study the properties and do more accurate predictions or forecasting on large scale social/other networks. Over a decade human engineered feature representations, based on network's structural and temporal properties, has been widely used in multiple tasks like node classification [1]}, characterizing information cascades [2]}. Although these works give promising results, they are task specific. The Skip-gram model proposed by Mikolov et al. [3]} has spurred lot of activity among research communities to automatically learn features from data, like networks. The end goal of all network embedding methods is to learn an encoding for the network nodes that effectively captures crucial properties of a node such as their neighborhood connectivity, degree, and position. Since such methods group together nodes that share same properties, these techniques are being used for variety of tasks like node and link prediction [4]}, [5]}, [6]} , node clustering [7]} and community detection [8]}.
